一个多策略改进的 rime优化算法用于三维USV路径规划和全球优化
Gaoquan Gu1, Jingjun Lou2, Haibo Wan1
1College of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan, 430033, Hubei, China.
多策略改进的RIME优化算法 (MIRIME) 通过解决缓慢的融合和局部优化来增强RIME. 在基准测试和无人驾驶地表车辆路径规划中,MIRIME表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 群集情报 群集情报 群集情报
背景情况:
- RIME优化算法 (RIME) 面临的挑战是缓慢的融合和局部优化.
- 现有的优化技术需要改进,以解决复杂的问题.
研究的目的:
- 为了开发一个多策略改进的RIME优化算法 (MIRIME).
- 为了提高全球优化,融合速度和人口多样性.
- 为了验证MIRIME在基准函数和实际路径规划问题上的有效性.
主要方法:
- 使用帐混乱地图进行初始化,以实现全局优化.
- 适应性更新策略,包括领导力和动态中位素,以改善利用.
- 镜头对人口多样性和趋同准确性的基于对立的学习进行成像.
- 中心边界控制策略,以提高搜索焦点和效率.
主要成果:
- 与CEC 2017和CEC 2022测试套件中的11个流行的算法相比,MIRIME表现出更好的性能.
- 该算法在无人驾驶地面车辆的3D路径规划中实现了更高的解决方案质量和稳定性.
- MIRIME有效地解决了原来的RIME算法的局限性.
结论:
- 在标准的RIME算法上,MIRIME提供了强大而有效的增强.
- 拟议的战略显著提高了融合速度,全球搜索能力和解决方案准确性.
- MIRIME显示出强大的实际应用潜力,特别是在复杂的路径规划场景中.
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